The innovation process: adoption of information and communication technology for the construction industry
Bibliographic record
Abstract
The paper discusses innovation and uses information and communication technology (ICT) as an example. A general framework that is broad in the perspectives it examines is presented for the analysis of innovations and technology adoption in the construction industry. The framework is described in relation to the life cycle of a technological innovation and consists of two primary perspectives: a macroview (top) and a microview (bottom). The analysis models either determine characteristics or measure values. Each analysis model is discussed in some detail and applied to an ICT example. The relevance of the framework is summarized by a discussion of how these interrelated analyses are applicable to the decision-making process within a particular firm and of the mechanisms required by the industry to improve the innovation process. A framework is required that is comprehensive in its ability to look at information and knowledge flows in support of innovation within the industry and at the interrelationships between micro and macro influences. Gaps in current approaches include a lack of quantitative analysis tools, the ability to reflect the dynamic aspect of innovation, and industry knowledge of practical decision-making tools. Key words: innovation, technology adoption, information and communication technology, construction engineering.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".